Embodiments of the present invention relate to neural monitoring and, more particularly, to an implantable wireless neural recording system.
Current research in electrophysiology and behavioral neuroscience strives to form a better understanding of the underlying principles of the brain of an animal or a human, and root causes of malfunction in its neuronal circuits. Ongoing research conducted on animal/human models demands systems that can simultaneously record neural signals from a large number of electrodes in awake, behaving animals/humans.
For decades, researchers have used racks of bulky data acquisition systems, connected to electrodes through a bundle of thin wires and a pre-amplifier headstage. This wired solution provides a wide bandwidth and is generally easy to use. Unfortunately, the wires can potentially affect the animal/human behavior by causing psychophysical tethering effects, and adding noise and motion artifacts to the recorded neural signals. To facilitate animal/human mobility and eliminate tangling and twisting of the wires, often a motorized commutator is used on top of the enclosure between the headstage and other instruments. A commutator is a delicate mechanical component and oftentimes the most expensive item in the system. The commutator is also the bottleneck for achieving large channel counts in hardwired setups, and limits experiments to only one animal/human at a time due to twisting and tangling of the wires. Accordingly, providing a natural and enriched environment for animal/human subjects in a hardwired setup is not feasible, either with or without a commutator. Consequently, neuroscientists are interested in replacing the wire bundles with a wireless link and continue recording and processing the “entire” neural signals in their high performance computing clusters without losing any information.
But thus far ongoing neural interfacing research is focused on transferring a limited processing capability to the implantable front-end to limit the required wireless bandwidth at the cost of losing valuable neural information and complicating the implantable unit. Such solutions typically consist of at least a transmitter and a receiver unit before digital signal processing. The transmitter is implanted inside or carried by the animal/human body. It is also responsible for conditioning (mainly amplification and filtering) of the acquired neural signals, and should include a power source with enough energy storage for the minimum duration of uninterrupted experiments.
Size, power consumption, robustness, input referred noise, and bandwidth are the main concerns in developing wireless neural recording (WNR) systems, and several groups have tried to tackle them in different ways. Nevertheless, WNR systems are still generally absent in electrophysiology labs. The majority of the neuroscientists are willing to adopt WNR systems if they can seamlessly substitute their current hardwired systems at a reasonable cost, setting up effort, maintenance, and additional support.
Currently systems of obtaining neural data and wirelessly transmitting same from the cranial cavity have significant drawbacks. A first conventional system of wirelessly recording and transmitting neural signals is to read the neural signal with an electrode and ultimately transmit the received signal wirelessly in an analog domain. There are many advantages of transmitting in the analog domain, including simplicity of the transmitter unit, preservation of the original waveform amplitudes, low power consumption to transmit information, and the ability to send large amounts of data in smaller packages. Unfortunately, the disadvantages of transmitting in analog outweigh its advantages. When transmitting in analog, the transmitted data is susceptible to noise and interference, which will ultimately reduce the quality of the transmitted signal and in some cases, renders it useless on the receiver side. There are many sources of noise and interference, some of which are internal within the system and some of are imposed from outside. Cross-talk among multiple recording channels, thermal noise, and oscillator phase noise are examples of internal noise. Electromagnetic interference from nearby sources of radio frequency, such as cell phones, and motion artifacts due to movements of the subject, are examples of external noise and interference. After the analog data is transferred, a receiver can convert the analog signal to a digital signal to be analyzed by a digital signal processing (DSP) system, or a computer. On the other hand, to resolve some of the above issues, a second conventional system of wirelessly recording and transmitting neural signals, or any other biological information, brings the analog to digital converter (ADC) block inside the body within the implantable device in order to convert the acquired data from analog format into digital, which is more robust against noise and interference. Digital conversion on the front end also provides the opportunity to compress the data and use the available wireless bandwidth more efficiently. Again, while there are a few advantages, such as more robust and reliable data transmission across the wireless link, the disadvantages outweigh the advantages. By implementing the ADC as part of the implantable device, the power consumption and size of the device increases and adds to the complexity of the implantable device. Digitized data bit stream at high data rates require very accurate timing and synchronization between the transmitter and receiver. If the receiver loses its synchronization with the transmitter, the received data will be lost and cannot be easily recovered. Furthermore, data compression on the transmitter side is not always desired, because it can potentially result in the loss of useful information.
For example, one current system employs commercial off-the-shelf components in their WNR system, particularly to establish the wireless link using the ZigBee and Bluetooth standards. Even though this method can significantly reduce the development time, and has the added benefit of complying with the Federal Communications Commission (FCC) regulations, the size and power overhead in general purpose components may lose their competitive edge in high channel counts.
Other exemplary systems have tried to tackle the bandwidth limitation by processing the neural signals on the transmitter unit by extracting their key features, and only sending a compressed version of the neural data across the wireless link. An important piece of information in a neural signal is the timing of the spike events. Hence, once spikes generated by a specific neuron are detected, one can transmit timing information, as opposed to the entire waveform. The challenge, however, is that the recorded signal from each extra cellular recording site contains spikes from a handful of nearby neurons that are randomly dispersed around the site, as well as those that are far away and their activities contribute to background noise. Spike neural activities should, therefore, be identified from noise and carefully sorted based on their waveforms before they can be converted to “single-unit” activities. To make things even more complicated, there are also gradual changes in the waveforms of the same neurons over time.
The processing power needed for the state-of-the-art spike sorting algorithms that operate on multiple channels in real time has required neuroscientists to employ high performance multi-core computing clusters. Embedding a comparable amount of computational power and programmability on the transmitter unit does not seem to be feasible, at least in the near future. Thus, such architectures are only suitable for neuroprostheses applications, where simple and low power routines would be sufficient. Moreover, many neuroscientists are interested not only in the single-unit activities but also in low frequency components of the neural signals, known as local field potentials (LFP), which are representative of the collective activities of thousands of neurons.
Yet another exemplary system can encode the neural signal amplitude above the noise level in a series of sharp pulses, which frequency is proportional to the signal amplitude. This is a low power encoding scheme and works well for a single or small number of channels. It is not clear, however, how the pulses generated from different channels would be combined to be transmitted across the wireless link.
Still other exemplary systems can combine the sampled neural signals from different channels using time division multiplexing (TDM) and transmitting an analog signal. The advantage of this method is its simplicity and low power consumption. Analog signals, however as mentioned earlier, are susceptible to noise, and the transitions from one channel to another in short sampling periods can result in significant crosstalk among adjacent channels on the receiver side.
To get a sense of how much information needs to be transferred across the wireless link, it is important to note that the neural signal spectrum spans from approximately 0.1 Hz to 10 kHz. Hence, the Nyquist rate requires 20 kilo-Samples per second per channel. Considering that recorded neural signals are often between 50 μV to 1 mV having a supply range of ±1.5 V, and the fact that even in a high quality recording there is often greater than 10 μV of background noise, a resolution of eight to ten bits should be sufficient in this application. Therefore, at least 160 kb/s of bandwidth is needed for raw data per recording channel. In other words, a 100 channel neural recording system, for example, requires a wireless link with 10 Mega-bits per second bandwidth.